Summary of Benchmarking Counterfactual Interpretability in Deep Learning Models For Time Series Classification, by Ziwen Kan et al.
Benchmarking Counterfactual Interpretability in Deep Learning Models for Time Series Classification
by Ziwen Kan, Shahbaz Rezaei, Xin Liu
First submitted to arxiv on: 22 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Medium Difficulty summary: This paper addresses the lack of benchmarking in counterfactual (CF) methods for time series data. CF methods identify minimal changes to alter model predictions. To address this gap, the authors redesign quantitative metrics to accurately capture desirable characteristics in CFs. A new metric set is introduced, combining validity, generation time, proximity, and redesigned metrics for sparsity and plausibility. The paper benchmarks 6 different CF methods on 20 univariate and 10 multivariate datasets with 3 classifiers, showing varying performance across metrics and models. The authors provide case studies and a guideline for practical usage. This work aims to improve interpretability in deep learning-based time series forecasting. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: This research study focuses on understanding how machine learning models make predictions about future events, like stock prices or weather forecasts. Right now, we don’t have a clear way to measure how well these models are doing this job. The authors of this paper are trying to fix that by creating new ways to compare different models and see which ones do the best job. They tested 6 different models on many different kinds of data and found that each model does better or worse depending on what we’re measuring. This study will help us understand how these models work and make them more useful for making predictions. |
Keywords
» Artificial intelligence » Deep learning » Machine learning » Time series
